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Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices

机译:利用太平洋和印度洋海表温度指数预测东非春季干旱

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In eastern East Africa (the southern Ethiopia, eastern Kenya and southernSomalia region), poor boreal spring (long wet season) rains in 1999, 2000,2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity andhigh levels of malnutrition. Predicting rainfall deficits in this region onseasonal and decadal time frames can help decision makers implement disasterrisk reduction measures while guiding climate-smart adaptation andagricultural development. Building on recent research that links morefrequent East African droughts to a stronger Walker circulation, resultingfrom warming in the Indo–Pacific warm pool and an increased east-to-west seasurface temperature (SST) gradient in the western Pacific, we show that thetwo dominant modes of East African boreal spring rainfall variability aretied to SST fluctuations in the western central Pacific and central IndianOcean, respectively. Variations in these two rainfall modes can thus bepredicted using two SST indices – the western Pacific gradient (WPG) andcentral Indian Ocean index (CIO), with our statistical forecasts exhibitingreasonable cross-validated skill (rcv ≈ 0.6). In contrast, thecurrent generation of coupled forecast models show no skill during the longrains. Our SST indices also appear to capture most of the major recentdrought events such as 2000, 2009 and 2011. Predictions based on thesesimple indices can be used to support regional forecasting efforts and land surfacedata assimilations to help inform early warning and guide climate outlooks.
机译:在东非东部(埃塞俄比亚南部,肯尼亚东部和索马里南部地区),1999年,2000年,2004年,2007年,2008年,2009年和2011年的北方春季降雨(雨季长)降雨不佳,造成了严重的粮食不安全和营养不良程度高。预测该地区在季节和年代际上的降雨不足,可以帮助决策者实施减少灾害风险的措施,同时指导气候智能适应和农业发展。基于近期的研究,该研究将印度-太平洋暖池的变暖和西太平洋东西向海表温度(SST)梯度增加所导致的东非频繁干旱与更强的沃克环流联系起来,我们发现这两种主要模式东非北方春季降水的多变性与西太平洋中部和印度洋中部海表温度的波动有关。因此,可以使用两个SST指数-西太平洋梯度(WPG)和印度洋中部指数(CIO)来预测这两种降雨模式的变化,而我们的统计预测显示出合理的交叉验证技能( r cv ≈0.6)。相比之下,当前的耦合预测模型的生成在长雨期间没有技能。我们的SST指数似乎也捕捉到了最近发生的大部分重大干旱事件,例如2000、2009和2011。基于这些简单指数的预测可用于支持区域预报工作和对地表数据的同化,以帮助提供预警和指导气候展望。

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